As students pursue a bachelor's degree in physics, they may ponder over which area to specialize in, such as theory, computation, or experiment. Often students develop preferences and dislikes, but it's unclear when this preference solidifies during their undergraduate experiences. To better understand, we interviewed eighteen physics majors at different stages of their degrees regarding their interest in theory, computation, and experimental methods. Out of the eighteen students, we analyzed only nine students who rated computation and theory the lowest. Our analysis did not include interest in the experiment because the ratings were less negative. We used Social Cognitive Career Theory (SCCT) and Lucidchart to analyze students' responses and create individual graphical representations of the influences for each student. Through this, we uncovered how various factors such as learning experiences, self-efficacy, and outcome expectations influenced their low interest in a particular method. We found that lack of knowledge and experience is often the main reason why self-efficacy was lower. Students' lack of interest is also influenced by negative outcome expectations (e.g., math-intensive and a bad work-life balance) more than other SCCT factors. Our findings could help physics departments and educators identify positive and negative factors that could lead to a more motivating and inclusive physics curriculum.

An understanding of vectors and vector operations is crucial for success in physics, as this serves as the foundation for various essential concepts, including motion and forces. Previous research indicates that only a fraction of introductory physics students have a usable knowledge of vectors and vector operations, and that more attention should be given to how students make sense of vectors. We examined classroom video data from an introductory physics course wherein students worked collaboratively through learning activities to introduce vectors and vector operations. During these activities, students' employment of gesture as a representational mode facilitated group sense-making. We propose a preliminary taxonomy of gestures for representing vector magnitudes, directions, and initial and terminal points. By identifying and characterizing the gestures used by students, we can gain insights into their learning processes and conceptual understanding of vectors, which can inform instructional design and teaching practices.

Analogies are known to be powerful tools for making sense of unfamiliar ideas in terms of already understood concepts. Science students regularly encounter unfamiliar ideas, such as microscopic objects that are invisible to our everyday experience and behaviors dictated by quantum mechanics. An understanding of basic concepts of quantum mechanics is useful in many disciplines, especially with the growing field of quantum information sciences and technologies. Physics researchers often use analogies in their own research and science communicators use them to make quantum ideas accessible to K-12 students and across STEM disciplines, but analogy use in upper-division teaching has been less researched. Our research goal is to understand how analogies are used to teach quantum mechanics, and specifically, what prior knowledge is used as a basis for analogies within two widely used quantum mechanics textbooks. This textbook analysis shows the most common bases for analogies include: mathematical structures from linear algebra, which are applied to model quantum systems; everyday life examples, which are used to make quantum systems more familiar and understandable; and macroscopic classical phenomena, which are used to highlight differences between classical and quantum mechanics. We also find authors use different conventions, based on the various cue words that authors use to indicate analogy-based reasoning. In the STEM classroom, this research has implications for enhancing student learning about abstract topics in science.

Designing physics courses that support students' activation and development of expert-like physics epistemologies is a significant goal of Physics Education Research. However, very little research has focused on how physics students' interactions with course structures resonate with different epistemological views. As part of a course redesign effort to increase student success in introductory physics, we interviewed introductory physics students about their experiences with course structures and their learning and belonging beliefs. We present here a case from this broader data corpus in which a student, Robyn, discusses his epistemological views of physics problem solving and his experiences with physics lectures, office hours, and discussion sections. We find that Robyn's physics epistemology manifests consistently across his interactions with each of these different course structures, suggesting a possible resonance between students' beliefs and their experiences with course structures and the value of further investigation into the potential merits of comprehensive course design.

In interviews with physics students and early career physicists, we ask about their experiences with having impairments in the physics setting and physics culture. In this paper, we highlight how experiences shared by participants as disabled people in physics represent clusters of models of disability. Specifically, we apply a theoretical framing of a three-dimensional disability model space, with axes defined as medical versus social (i.e., cause); tragedy versus affirmative (i.e., effect); and minority group versus universal (i.e., ability/disability dichotomy). For example, in this framework, providing accommodations is described by a cluster of the social and minority models of disability. By analyzing participants' experiences in physics through this disability framework, we aim to identify the models that underpin supportive experiences and support the development of policies and professional development for the physics community towards benefiting disabled people. Through analysis and comparison of these models and participants' narratives, we offer a discussion and possible guidelines for instructors interacting with students with disabilities, opportunities for those with disabilities to deconstruct their own prior experiences and analyze potential misinterpretations that may arise from the models.

Learning using Computer-Assisted Instruction (CAI) demands a high level of attention given the tendency to be distracted and mind-wander. How does the online STEM instructor know when learners are having attentional problems and the extent to which these problems affect learning? In the present study, the visual attentional and cognitive state of physics graduate students were probed while they went through a multimedia instructional module to refresh their knowledge of Newton's II Law. Data from an eye tracker, webcam, egocentric glasses, screen recording, and mouse and keyboard events were integrated to record learners' attention overt attention to the learning environment (+/-) and thinking about learning content (+/-) to analyze students' attention spans during learning from this module. On average, learners were found to be on-task and on-screen for a vast majority of time, with evidence of mind wandering. The learning module improved the participants efficiency with which they answered the questions correctly on a post-test relative to the pre-test. Further, there is a positive albeit statistically non-significant correlation between the improvement from pre- to post-test efficiency and the time spent on-screen and on-task during the module.

Students with disabilities involved in postsecondary physics education may benefit from research opportunities and mentorship. However, the literature documenting supports provided by physics mentors to disabled students is limited. In this study, we analyze interviews with five mentors who either instruct physics courses or lead a research group for examples of how they support disabled students doing research or seeking career advice. Furthermore, we contextualize the examples of supports using six models of disability. Models include the cause of disability (medical/social), the effect of impairment on well-being (tragedy/affirmative), and the dichotomy of dis/ability (minority/universal). We find mentors discuss supports provided to disabled students in research settings that align with clusters of models of disability. While there is not one set of models that yields a one-size-fits-all solution, the universal model plus social model cluster can help mentors design useful and durable supports.

A practical and well-studied method for computing the novelty of a design is to construct an ordinal embedding via a collection of pairwise comparisons between items (called triplets), and use distances within that embedding to compute which designs are farthest from the center. Unfortunately, ordinal embedding methods can require a large number of triplets before their primary error measure-the triplet violation error-converges. But if our goal is accurate novelty estimation, is it really necessary to fully minimize all triplet violations? Can we extract useful information regarding the novelty of all or some items using fewer triplets than classical convergence rates might imply? This paper addresses this question by studying the relationship between triplet violation error and novelty score error when using ordinal embeddings. Specifically, we compare how errors in embeddings produced by Generalized Non-Metric Dimensional Scaling (GNMDS) converge under different sampling methods, for different numbers of embedded items, sizes of latent spaces, and for the top K most novel designs. We find that estimating the novelty of a set of items via ordinal embedding can require significantly fewer human-provided triplets than is needed to converge the triplet error, and that this effect is modulated by the type of triplet sampling method (random versus uncertainty sampling). We also find that uncertainty sampling causes unique converge behavior in estimating most novel items compared to non-novel items. Our results imply that in certain situations one can use ordinal embedding techniques to estimate novelty error in fewer samples than is typically expected. Moreover, the convergence behavior of top K novel items motivates new potential triplet sampling methods that go beyond typical triplet reduction measures.

AI-driven personalized support can help students learn from Open-Ended Learning Environments (OELEs). In this paper, we focus on how to effectively provide repeated hints in OELEs, when students repeat a sub-optimal behavior after receiving a hint on how to recover from the first occurrence of the behavior. We formally compare two repeated hint designs in UnityCT, an OELE that fosters Computational Thinking (CT) via free-form game design in K-6 education, with the long-term goal of providing AI-driven personalized hints. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Early mathematics skills relate to later mathematics achievement and educational attainment, which in turn predict career choice, income, health and financial decision-making. Critically, large differences exist among children in early mathematics performance, with parental mathematics engagement being a key predictor. However, most prior work has examined mothers' mathematics engagement with their preschool- and school-aged children. In this Registered Report, we tested concurrent associations between mothers' and fathers' engagement in mathematics activities with their 2- to 3-year-old toddlers and children's mathematics performance. Mothers and fathers did not differ in their engagement in mathematics activities, and both parents' mathematics engagement related to toddlers' mathematics skills. Fathers' mathematics engagement was associated with toddlers' number and mathematics language skills, but not their spatial skills. Mothers' mathematics engagement was only associated with toddlers' mathematics language skills. Critically, associations may be domain-specific, as parents' literacy engagement did not relate to measures of mathematics performance above their mathematics engagement. Mothers' and fathers' mathematics activities uniquely relate to toddlers' developing mathematics skills, and future work on the nuances of these associations is needed.